WEBVTT 1 00:00:04.200 --> 00:00:09.119 The information economy as a rod. The world is teaming with innovation as new 2 00:00:09.160 --> 00:00:15.400 business models reinvent every industry industry. Inside Analysis is your source of information and 3 00:00:15.599 --> 00:00:19.800 insight about how to make the most of this exciting new era. Learn more 4 00:00:19.960 --> 00:00:25.000 and inside Analysis dot Comside Analysis dot com. And now here's your host, 5 00:00:25.480 --> 00:00:36.759 Eric Kavanaugh. Yes, oh, yes, players and gentlemen's hello and welcome 6 00:00:36.799 --> 00:00:40.520 back once again to the only coast to coast radio show that's all about the 7 00:00:40.560 --> 00:00:45.479 information economy. It's called Inside Analysis. Yours truly, Eric Kavanaugh here with 8 00:00:45.600 --> 00:00:49.600 a very special show today, folks, and a very special guest. We're 9 00:00:49.600 --> 00:00:54.039 gonna be talking to Bob Muglia, the former CEO of Snowflake and an all 10 00:00:54.039 --> 00:00:57.479 around innovator in our industry for many, many years. And he's got a 11 00:00:57.479 --> 00:01:00.280 new book along with Steve Hamm, a book called The Data Preneurs, The 12 00:01:00.359 --> 00:01:06.319 Promise of AI and the Creators Building our Future. And you know, I'm 13 00:01:06.319 --> 00:01:11.120 sitting here in Gibsonia, Pennsylvania, and I'm inclined to come up with a 14 00:01:11.120 --> 00:01:15.680 great quote from one of my personal heroes, someone I follow and learn from, 15 00:01:15.719 --> 00:01:19.319 William Gibson, who was a futurist and he had a great quote where 16 00:01:19.319 --> 00:01:23.040 he said the future is here already, it's just not evenly distributed, and 17 00:01:23.079 --> 00:01:26.079 I was like, Holy Christmas, what kind of a quote is that? 18 00:01:26.519 --> 00:01:30.719 And what he means is that there are pockets of innovation. There are pockets 19 00:01:30.760 --> 00:01:34.000 like in cities, for example, where you have more cell phones and cell 20 00:01:34.040 --> 00:01:38.319 towers and five G more laptops and iPads and things of this nation. And 21 00:01:38.400 --> 00:01:41.239 you go way out of the country and maybe not so much. You have 22 00:01:41.359 --> 00:01:46.239 more factory equipment, for example, or mills or mines or different things of 23 00:01:46.280 --> 00:01:49.920 this nature, and it's a different story, but you do have these pockets 24 00:01:49.959 --> 00:01:53.000 of innovation. Of course, it also means the past is all around us 25 00:01:53.040 --> 00:01:56.719 as well, right it's not just the future. But what's cool about the 26 00:01:56.799 --> 00:02:00.400 time we're in right now is that this is a very significant changing of the 27 00:02:00.480 --> 00:02:05.480 guard, if you will. You look at what artificial intelligence is now doing, 28 00:02:05.519 --> 00:02:08.319 and it's not new. We've had AI for forty fifty years. The 29 00:02:08.439 --> 00:02:10.879 term has been around for a long time. The algorithms, some of them 30 00:02:10.960 --> 00:02:15.520 have been around for a long time. But what we didn't have was the 31 00:02:15.599 --> 00:02:19.759 compute. But we didn't have was the data to train these algorithms and we 32 00:02:19.800 --> 00:02:23.800 didn't have the force of business to push it through, and now we certainly 33 00:02:24.039 --> 00:02:29.879 do so we've kind of seen these worlds of data management and artificial intelligence and 34 00:02:29.960 --> 00:02:34.000 machine learning really start to coalesce in the last few years. And then, 35 00:02:34.039 --> 00:02:38.960 of course chat GPT rolled out their large language model, which is blowing minds, 36 00:02:38.080 --> 00:02:42.960 and then a couple of companies did too. Google even Data Bricks rolled 37 00:02:42.960 --> 00:02:46.759 out a large language model in fairly short order after chat GBT hit the scene. 38 00:02:47.080 --> 00:02:50.960 And there were some strange things in the news, like The New York 39 00:02:51.000 --> 00:02:55.639 Times having a reporter talked to the chat GBT and it told him to leave 40 00:02:55.680 --> 00:02:59.639 his wife and all this crazy stuff. But I would argue that was a 41 00:02:59.680 --> 00:03:04.520 miss use of the technology. You shouldn't be trying to tease weird answers out 42 00:03:04.520 --> 00:03:07.719 of a large language model. It's there to reflect back the knowledge that is 43 00:03:07.840 --> 00:03:13.120 extant in the world today, and it can do so in a grammatically correct, 44 00:03:13.199 --> 00:03:16.599 syntactically compelling sort of way. It can write proposals, it can write 45 00:03:16.639 --> 00:03:20.840 tweets, it can write articles and blogs and all sorts of different things. 46 00:03:20.840 --> 00:03:23.759 It does hallucinate, it does come up with stuff because it's really just parsing 47 00:03:23.879 --> 00:03:28.840 little fragments of content that it's found somewhere else on the web. So there 48 00:03:28.840 --> 00:03:31.400 are lots of interesting things to get into around all that. But today we 49 00:03:31.479 --> 00:03:36.159 wanted to help Bob promote his new book. I've really learned about the impetus 50 00:03:36.199 --> 00:03:39.319 behind this and understand more about what makes Bob ticks. So with that, 51 00:03:39.360 --> 00:03:44.000 Bob mowgli All, welcome to Inside Analysis. Congrats on your book. I 52 00:03:44.000 --> 00:03:46.039 think it's very exciting. I'm looking over it over the weekend. It must 53 00:03:46.080 --> 00:03:50.120 feel good to put that to bed and begin the promotion process, right, 54 00:03:50.560 --> 00:03:52.879 It does feel good. It's been a lot of work. I've been working 55 00:03:52.879 --> 00:03:55.719 on it for a couple of years. When Steve and I started on this, 56 00:03:55.879 --> 00:04:00.680 the really idea was is that I had some things I wanted to say. 57 00:04:00.719 --> 00:04:03.319 I thought there's some things I could help teach people from my career, 58 00:04:03.759 --> 00:04:08.240 and I wanted to say it in a way that was easy for people to 59 00:04:08.280 --> 00:04:12.479 read and enjoyable. And so we came up with the idea of talking about 60 00:04:12.759 --> 00:04:17.120 technology and the advances in data from the perspective of the entrepreneurs of data, 61 00:04:17.160 --> 00:04:20.839 the people who have made it happen really over the last forty years. And 62 00:04:21.160 --> 00:04:25.399 I've had the incredible fortune to be able to work with quite a number of 63 00:04:25.399 --> 00:04:28.759 these folks, and so we wanted to highlight those folks, the so called 64 00:04:28.879 --> 00:04:32.759 datapreneurs. We're able to quickly shorten the name from the data entrepreneurs to the 65 00:04:32.800 --> 00:04:39.920 datapreneurs a little bit catch here. I hope to highlight their work starting, 66 00:04:39.920 --> 00:04:43.120 you know, really in the early days of Sequel, the early days of 67 00:04:43.240 --> 00:04:47.519 databases, going through the Internet era, the work that was done at Microsoft 68 00:04:47.560 --> 00:04:53.199 around information at your fingertips, and then all of the advancements that we've seen 69 00:04:53.240 --> 00:04:57.279 in the modern data stack in the last ten years. And when we started 70 00:04:57.279 --> 00:05:00.800 the book, really we knew that, you know, I knew that machine 71 00:05:00.879 --> 00:05:04.920 learning was making a significant amount of changes in the world. I saw that 72 00:05:04.959 --> 00:05:09.600 it was having an impact. It was working with new types of data that 73 00:05:09.639 --> 00:05:14.480 we couldn't work on before. For example, images or videos are it was 74 00:05:14.480 --> 00:05:17.279 always data that was relatively opaque to computer's hard to work with. All of 75 00:05:17.319 --> 00:05:20.720 a sudden, this machine learning was beginning to crack it. But we really, 76 00:05:21.120 --> 00:05:25.240 I did not realize how fast this stuff was going to advance. To 77 00:05:25.240 --> 00:05:28.759 be honest, I was, I think, like most of the colleagues I 78 00:05:28.800 --> 00:05:31.360 know, have been caught a little bit flat floated by the speed of advancement 79 00:05:31.959 --> 00:05:35.879 that we've seen in the last eighteen months. And I continue to be kind 80 00:05:35.879 --> 00:05:42.240 of blown away by how fast the innovation is happening right now. Well, 81 00:05:42.480 --> 00:05:45.199 sure, and you know what's really cool here, and I'll try to keep 82 00:05:45.240 --> 00:05:48.879 the focus on the people and the technology, is that people use technology right 83 00:05:48.920 --> 00:05:53.920 there. I look at the media in general and the mainstream media, and 84 00:05:53.920 --> 00:05:57.000 I'm a huge media fan. I'm in the media. Obviously it can learn 85 00:05:57.000 --> 00:05:59.639 a lot from the media. But I've got one of these mantras I throw 86 00:05:59.680 --> 00:06:03.439 out there to people that the narrative is always wrong. And what I mean 87 00:06:03.480 --> 00:06:08.480 by that is the narrative, by definition, is a story, and so 88 00:06:08.519 --> 00:06:13.240 it's going to have some flare to it and some embellishment, for example. 89 00:06:13.279 --> 00:06:16.000 But for some reason the narrative just gets off track and it's hard to get 90 00:06:16.000 --> 00:06:19.319 it back on track. And what I'm talking about here is the narrative that 91 00:06:19.399 --> 00:06:24.439 AI is the red eyed robot that's going to take over the world and all 92 00:06:24.480 --> 00:06:27.680 this stuff, and that's just not the case. I mean, I don't 93 00:06:27.720 --> 00:06:30.759 think these large language models are going to take over the world anytime soon. 94 00:06:30.040 --> 00:06:33.560 But The key is to understand the technology and know how to use it, 95 00:06:33.680 --> 00:06:38.480 know how to leverage it for your business, and that's where the magic happens. 96 00:06:38.519 --> 00:06:41.120 So just this morning, I was talking to one of our partners, 97 00:06:41.120 --> 00:06:44.000 Eve Mulgars from seven W Data. You've met him I form one of our 98 00:06:44.040 --> 00:06:46.319 webinars, and he said, what he's learned about CHAT, GBT, these 99 00:06:46.399 --> 00:06:50.519 large language models is you have to be very explicit when telling it what you 100 00:06:50.560 --> 00:06:55.639 want. So it's just like a requirements documentary kind of going through the process 101 00:06:55.639 --> 00:06:58.199 telling you I want A but not B and not C, but D and 102 00:06:58.279 --> 00:07:00.759 not and not F and give it to me in this style. And the 103 00:07:01.160 --> 00:07:05.240 closer you can get to articulating the policy if you will, which is the 104 00:07:05.279 --> 00:07:09.519 prompts, the better results you're going to get from the technology. And that's 105 00:07:09.519 --> 00:07:14.000 true across the board. Understanding how to use the technology and then doing it 106 00:07:14.000 --> 00:07:17.839 correctly, that's what leads to big success, right absolutely, and right now 107 00:07:17.959 --> 00:07:21.800 people are doing a lot of that prompt engineering to help to get the more 108 00:07:21.839 --> 00:07:27.199 advanced models like GPT four, GPT three five to do exactly what they want. 109 00:07:27.720 --> 00:07:30.839 And we'll see more of that over time as these things become more sophisticated. 110 00:07:30.879 --> 00:07:35.560 The newer models are starting to do things like allowing you to begin to 111 00:07:35.920 --> 00:07:41.000 reason. They actually have reasoning capabilities. It is pretty amazing to start to 112 00:07:41.040 --> 00:07:45.920 see the computers can do something. There's a higher order intelligence that has always 113 00:07:45.920 --> 00:07:48.560 been reserved for humans, and even starting to be able to do simple planning. 114 00:07:48.560 --> 00:07:51.759 The planning isn't its advanced as it will be in the next few years, 115 00:07:51.800 --> 00:07:55.920 but they're starting to do that. The other thing that's fascinating, and 116 00:07:55.959 --> 00:07:59.319 this is I mean, I think is one of the most incredible things that's 117 00:07:59.319 --> 00:08:07.199 happened in the past two months is there's a veritable Cambrian explosion of open source 118 00:08:07.319 --> 00:08:13.839 development happening, with people really all across the world contributing to improve the algorithmic 119 00:08:13.920 --> 00:08:20.519 structure of models to allow allow models to run on much smaller computers to be 120 00:08:20.560 --> 00:08:24.199 inferenced without having these big data centers behind them, so they could actually run 121 00:08:24.240 --> 00:08:28.560 in portable systems, being able to run with much less memory and much and 122 00:08:28.720 --> 00:08:33.039 use a lot less power and just be much more efficient in general. Now 123 00:08:33.039 --> 00:08:37.840 those models are not yet as sophisticated as the bespoke models like a GPT four 124 00:08:39.039 --> 00:08:41.720 is, but the improvement that we've seen in the last three months is pretty 125 00:08:41.759 --> 00:08:46.720 remarkable, and a lot of organed companies are taking advantage of this. One 126 00:08:46.759 --> 00:08:48.440 of the things that I think has been answered, and this was not clear 127 00:08:48.720 --> 00:08:52.720 like four or five months ago, is you know, will this technology be 128 00:08:52.840 --> 00:08:56.720 something that is available? Is it a new digital divide going to be created 129 00:08:56.759 --> 00:09:03.039 with this technology really only be available to the elite few. And I think 130 00:09:03.080 --> 00:09:07.120 it's pretty clear that the cost of intelligence is going to rapidly move to is 131 00:09:07.200 --> 00:09:11.799 close to zero. The cost of this artificial intelligence will go down substantially, 132 00:09:11.840 --> 00:09:15.559 so it will be accessible to pretty much everybody, which I think is incredibly 133 00:09:15.600 --> 00:09:18.440 good. I think it's I was worried if we were going to be in 134 00:09:18.480 --> 00:09:24.360 a situation where the development of these large language models and advanced artificial intelligence was 135 00:09:24.399 --> 00:09:28.960 going to be kept within a handful of companies. Sure, I think it's 136 00:09:28.000 --> 00:09:31.840 far far better if we have a large number of these things being created. 137 00:09:33.159 --> 00:09:35.840 You know, it's funny you should say all that, because I'm always trying 138 00:09:35.840 --> 00:09:39.080 to explain things to my wife and friends and other folks, and I brought 139 00:09:39.120 --> 00:09:41.840 up exactly what you just said. I said, the one danger would be 140 00:09:41.960 --> 00:09:46.759 if there is some way that a handful of corporations which have access to these 141 00:09:46.799 --> 00:09:52.519 technologies, think Quantum, think IBM with access to these quantum machines, and 142 00:09:52.600 --> 00:09:54.679 something a lot of people don't understand is that's going to be a very, 143 00:09:54.799 --> 00:10:01.480 very disruptive development when those machines go mainstream, right, or at least theoretically 144 00:10:01.480 --> 00:10:03.240 they will be. I was talking to a guy from Harmon Cardony of the 145 00:10:03.320 --> 00:10:07.360 Day at the click World conference, and he was pointing out that when this 146 00:10:07.399 --> 00:10:11.600 stuff goes mainstream, when quantum goes mainstream, all of the cryptography falls sideways, 147 00:10:11.720 --> 00:10:15.360 like you'll be able to hack into any kind of system anywhere. So 148 00:10:15.480 --> 00:10:18.559 well, what does that mean? But I think you're right that it is. 149 00:10:18.639 --> 00:10:22.519 And open source is the reason why I'm a huge open source fan. 150 00:10:22.600 --> 00:10:26.480 From the moment I learned what it was about, I did a whole bunch 151 00:10:26.519 --> 00:10:28.200 of research. This is back in two thousand and five, and I remember 152 00:10:28.240 --> 00:10:33.440 at the time, the Apache web server had just eclipsed the Internet Explorer whatever 153 00:10:33.440 --> 00:10:37.320 it was called. The Microsoft oh I was called Internet Explore. It was 154 00:10:37.360 --> 00:10:43.279 called Internet Explorer, Okay, So I asked, I asked, Internet Information 155 00:10:43.320 --> 00:10:46.360 Server was web server? That's right? And Apache passed them, And I 156 00:10:46.399 --> 00:10:50.759 thought to myself, and at the time, I was studying service oriented architecture 157 00:10:50.320 --> 00:10:54.840 and thinking to myself, wait a minute, the nexus of open source as 158 00:10:54.879 --> 00:10:58.960 a methodology, as a discipline and soa this spells trouble for the traditional cloe 159 00:11:00.279 --> 00:11:03.720 source big box vendors like the Oracles and the SAPs of the world's going to 160 00:11:03.799 --> 00:11:07.720 shake out of all this, And it took about nine years longer than I 161 00:11:07.759 --> 00:11:09.519 thought. But then all of a sudden I looked around and there are all 162 00:11:09.559 --> 00:11:13.480 these open source vendors everywhere. There's a whole stack built for data. Of 163 00:11:13.519 --> 00:11:16.879 course, the whole had dupe thing we've talked about in the past, and 164 00:11:16.440 --> 00:11:22.000 of Apache, Spark and all these different things, and it really fundamentally changed 165 00:11:22.039 --> 00:11:26.879 the game about how software is developed and produced and managed. And I think 166 00:11:26.879 --> 00:11:30.240 it became a much more collegial environment, which is good for all of us. 167 00:11:30.279 --> 00:11:33.559 What do you think, Well, I'm a huge fan of open source. 168 00:11:33.720 --> 00:11:35.600 It's interesting because my background, of course, was a Microsoft background, 169 00:11:35.639 --> 00:11:39.399 and certainly in the old days, Microsoft wasn't that open to open source. 170 00:11:39.960 --> 00:11:43.759 That was certainly true, although I felt like I was always on the other 171 00:11:43.799 --> 00:11:46.360 side of that of that particular battle. That was one that Stephen Bill held 172 00:11:46.360 --> 00:11:52.519 fairly close to They had some very strong feelings about rietary software, and I 173 00:11:52.519 --> 00:11:56.039 think that was expressed in number of times. I was generally speaking on the 174 00:11:56.039 --> 00:11:58.799 other side of that, although I built You know, it was funny because 175 00:11:58.840 --> 00:12:01.919 you mentioned information Server, that was my product, and I watched it get 176 00:12:03.000 --> 00:12:11.559 killed, largely because we had a security vulnerability that just you could see. 177 00:12:11.600 --> 00:12:16.320 You could see the trajectory totally changed when Microsoft was attacked, Our software was 178 00:12:16.360 --> 00:12:20.080 attacked, and how all of a sudden it became something that almost nobody wanted 179 00:12:20.120 --> 00:12:24.639 to use at that point. But the general trend towards the Internet using open 180 00:12:24.679 --> 00:12:28.039 source was one that was inexorable, and certainly we've seen it took Microsoft a 181 00:12:28.080 --> 00:12:33.279 long time, for example, to give up their proprietary browser and moved to 182 00:12:33.360 --> 00:12:39.279 Chromium as at as an open source browsing alternative. You know, the engine 183 00:12:37.879 --> 00:12:45.480 inside Edge is now essentially the same engine it's inside Chrome, and that just 184 00:12:45.600 --> 00:12:50.559 means that open source has won that battle. It's interesting the language battle is 185 00:12:50.639 --> 00:12:54.240 not yet understood. I don't think that there is clarity in the industry is 186 00:12:54.240 --> 00:12:58.720 what's going to happen. Certainly, if you talk to the Microsoft folks or 187 00:12:58.759 --> 00:13:03.120 the open AI folks, they will continue to say that that building bespoke sort 188 00:13:03.120 --> 00:13:07.759 of proprietary models that are very that are built with very very large amounts of 189 00:13:07.799 --> 00:13:13.120 compute, that that's going to continue to be a way of staying ahead. 190 00:13:13.240 --> 00:13:16.559 I think they still they believe that, they certainly have believed that, and 191 00:13:16.600 --> 00:13:22.679 certainly they've achieved They've achieved the pre eminent model through that approach um Opening Eye 192 00:13:22.720 --> 00:13:28.799 has demonstrated that, uh that larger that they're building very large models. You 193 00:13:28.799 --> 00:13:31.799 know, we you know, to a GPT four roughly we don't know exactly, 194 00:13:31.840 --> 00:13:35.559 but roughly a trillion parameters in that model. It can take on and 195 00:13:35.600 --> 00:13:41.639 do some things that that that other models can't do. But it's fascinating because 196 00:13:41.840 --> 00:13:48.159 the techniques that are being developed are really quite significant in the open source community, 197 00:13:48.360 --> 00:13:50.879 and I think they're going to have to have an impact on the Googles 198 00:13:50.879 --> 00:13:54.480 and the Opening Eyes and the Metas of the world as they continue to develop 199 00:13:54.519 --> 00:14:03.120 their large their large models. Having having a big companies spend money to on 200 00:14:03.279 --> 00:14:07.799 compute to build the fundamental language foundation in these models is going to be somewhat 201 00:14:07.840 --> 00:14:13.080 necessary for a while. There is open source solutions being built just just open 202 00:14:13.120 --> 00:14:16.600 sourcing all the data and everything else. Um, But I do think there'll 203 00:14:16.600 --> 00:14:20.840 be a balance of these things. Meta did some really good things by by 204 00:14:20.960 --> 00:14:26.000 releasing their model to the to the research community, and then everything wound up 205 00:14:26.080 --> 00:14:30.679 leaking to the open source community, and essentially that's what really set off this 206 00:14:30.679 --> 00:14:33.360 this advancement in the open source world. But again, I think it's a 207 00:14:33.360 --> 00:14:37.279 great thing. I think it's a great thing to see lots of models developing. 208 00:14:37.320 --> 00:14:43.720 As I work with companies that are that are innovating in the artificial intelligence 209 00:14:45.080 --> 00:14:48.519 natural language space, you know, I do see people using a mixture of 210 00:14:48.559 --> 00:14:52.240 these models. GPT four is still the best if you're working with more complex 211 00:14:52.240 --> 00:14:56.879 sorts of questions, but if you if you're the problem you're trying to solve 212 00:14:56.919 --> 00:15:03.360 this more specific these these open source models are actually achieving equal quality to GPT 213 00:15:03.480 --> 00:15:07.279 for it in more isolated cases, and they do so at a fraction of 214 00:15:07.360 --> 00:15:11.600 the compute compute costs, which means that they're cheaper to run and just generate 215 00:15:11.679 --> 00:15:16.279 less carbon. These models could generate a lot of carbon potentially, they're they're 216 00:15:16.440 --> 00:15:20.039 using a lot of compute horsepower right now. Well. And one nice thing 217 00:15:20.080 --> 00:15:26.960 about open source is that we can all collaborate on building foundations such that individual 218 00:15:26.000 --> 00:15:31.360 companies and people can then finish off their last model, which is what makes 219 00:15:31.360 --> 00:15:35.399 their company special. And if you collaborate around the foundation, you can tackle 220 00:15:35.440 --> 00:15:41.440 a lot of really basic issues that become incumferences otherwise, like just being able 221 00:15:41.440 --> 00:15:45.200 to move data from place to place, being able to connect to the networking 222 00:15:45.200 --> 00:15:50.200 side of equations. There are so many foundational components to compute, which, 223 00:15:50.279 --> 00:15:54.720 if standardized and open sourced, will allow us to really focus on that last 224 00:15:54.799 --> 00:15:58.000 mile of innovation that makes us special. What do you think? I totally 225 00:15:58.000 --> 00:16:03.080 agree, And again I think we're seeing that happen given the advancement in the 226 00:16:03.840 --> 00:16:07.360 very rapid innovation that's happened literally since February. I mean we're just talking three 227 00:16:07.399 --> 00:16:14.360 months, it's ridiculous, and seeing the sets of fundamental changes that are coming 228 00:16:14.360 --> 00:16:15.720 out and you know, reducing the time to train. I mean, give 229 00:16:15.759 --> 00:16:18.799 you an example. One of the companies I work with, Documgami is uses 230 00:16:19.120 --> 00:16:26.679 their own model to pull data out of business contracts and for them and their 231 00:16:26.720 --> 00:16:30.840 customers, they want to they don't want to send their customers don't want to 232 00:16:30.840 --> 00:16:33.240 send all the data up to open AI and have it run through GPT four. 233 00:16:33.559 --> 00:16:37.840 They want to keep that more proprietor and they also don't want to deal 234 00:16:37.879 --> 00:16:41.840 with any kind of hallucinations on this. So the models, the models that 235 00:16:41.879 --> 00:16:48.399 they're using are are tuned and focused on working directly with customer information exclusively and 236 00:16:48.480 --> 00:16:52.120 focused on only pulling data out of the customer documents and the way they don't 237 00:16:52.159 --> 00:16:56.639 imagine something. They don't make anything up but the accurate. But they're able. 238 00:16:56.679 --> 00:17:02.120 They were able to train a model that for some of their use cases 239 00:17:02.720 --> 00:17:07.200 it met or exceeded GPT four A capability and it was under ten thousand dollars 240 00:17:07.359 --> 00:17:12.400 in it. Called fine tuning costs, this is where you take a model 241 00:17:12.440 --> 00:17:17.160 that has been already trained, an existing open source model, and then you 242 00:17:17.200 --> 00:17:21.680 fine tune it with you expose it to the documents that are very specific to 243 00:17:21.720 --> 00:17:25.680 the industry that you're head. And so just for a small, relatively incremental 244 00:17:25.720 --> 00:17:30.960 cost to build something at pretty high accuracy rates, Yeah, that's a really 245 00:17:30.039 --> 00:17:34.240 big deal. And once again we're talking about opening the kimono, if you 246 00:17:34.279 --> 00:17:38.880 will, showing people what's going on, allowing them to collaborate around different component 247 00:17:38.960 --> 00:17:41.759 parts of the stack and that's what you get in the open source world. 248 00:17:41.799 --> 00:17:45.519 You have all these projects that do specific things. And of course it started 249 00:17:45.519 --> 00:17:48.599 with Linux, the operating system climbed up the stack. Now we have all 250 00:17:48.640 --> 00:17:52.519 sorts of things, a patchie Spark, for example, a patchy kafta, 251 00:17:52.680 --> 00:17:56.480 which was the engine that ran LinkedIn now that's been open source. These are 252 00:17:56.480 --> 00:18:00.680 amazing technologies that are changing in the world. Pick it up right out at 253 00:18:00.680 --> 00:18:03.519 the right, don't touch up doll your lift thing to info. What if 254 00:18:03.559 --> 00:18:07.039 you could own a piece of the future. 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Don't throw away 297 00:21:33.440 --> 00:21:37.039 your money on maintenance fees, use it for things you really want. We 298 00:21:37.119 --> 00:21:41.039 can help you end your time share contract and stop the money drain immediately. 299 00:21:41.279 --> 00:21:45.839 If you are ready to move on with your time share, call our team 300 00:21:45.200 --> 00:21:49.400 right now. Cancel your time Share now with a free call. Eight hundred 301 00:21:49.440 --> 00:21:55.640 two eight nine O four one three eight hundred two eight nine O four one 302 00:21:55.839 --> 00:22:03.480 three eight hundred two eight nine O four one three hundred two eight nine zero 303 00:22:00.960 --> 00:22:10.759 four. Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh. 304 00:22:15.400 --> 00:22:18.839 All right, folks, back here on Inside Analysis. Yes, take us 305 00:22:18.839 --> 00:22:22.119 to the future. Indeed, that's what AI is doing right now. It's 306 00:22:22.160 --> 00:22:26.279 taking us to the future, a future in which we can get a lot 307 00:22:26.319 --> 00:22:30.920 more done. This is a new tool, these AI engines. There are 308 00:22:30.920 --> 00:22:33.839 new tools that we can leverage. The large language models, of course, 309 00:22:33.839 --> 00:22:37.920 for getting all the headlines. That's not the only form of artificial intelligence out 310 00:22:37.920 --> 00:22:38.920 there. But you know, I did have a thought as you were talking. 311 00:22:38.960 --> 00:22:42.319 We're talking to Bob Muglia, who has a new book that is out 312 00:22:42.400 --> 00:22:45.039 right now, very cool stuff. I took a look at it over the 313 00:22:45.079 --> 00:22:49.079 weekend, the data preneurs, the promise of AI and the creators building our 314 00:22:49.160 --> 00:22:55.359 future, and you know what I thought, that's quite interesting and ironic, 315 00:22:55.440 --> 00:23:00.240 perhaps, Bob, is that this AI power is going to do two things 316 00:23:00.240 --> 00:23:03.119 simultaneously. One, it's going to make, of course, deep fakes much 317 00:23:03.200 --> 00:23:06.240 easier. But too, I think it is, if implemented properly, going 318 00:23:06.279 --> 00:23:10.200 to affect much greater transparency. These are two different things, right, What 319 00:23:10.359 --> 00:23:12.200 is real and what is fake are two different things. It's good at creating 320 00:23:12.200 --> 00:23:15.400 the fake stuff, but I think it's also going to be good at generating 321 00:23:15.519 --> 00:23:18.920 transparency such that we have trust in what we're looking at. What do you 322 00:23:18.920 --> 00:23:22.119 think about that? Well, I think it's going to force us to work 323 00:23:22.160 --> 00:23:26.279 together in a lot of different ways than we have ever worked before. It'll 324 00:23:26.279 --> 00:23:30.000 do stuff it's going to do. I think in the short run, what 325 00:23:30.039 --> 00:23:33.720 AI will do is it's going to help automate the types of tasks that people 326 00:23:33.759 --> 00:23:38.200 don't really enjoy doing. And sometimes they're not all that good at doing because 327 00:23:38.200 --> 00:23:44.279 they're repetitive tasks and people just get bored doing them. So we'll see a 328 00:23:44.319 --> 00:23:49.920 lot of improvement in the way businesses work. I think almost every application will 329 00:23:51.000 --> 00:23:55.720 change in the next three to five years to incorporate AI. Honestly, from 330 00:23:55.720 --> 00:24:00.799 my perspective, it's the largest and most significant thing that's happened and technology in 331 00:24:00.880 --> 00:24:04.279 my lifetime. I think the fact that all of a sudden we have computers 332 00:24:04.319 --> 00:24:10.799 that can parse and really in some senses, understand English in any language, 333 00:24:10.799 --> 00:24:14.000 not just English, any native language. Really they're quite good at all it's 334 00:24:14.079 --> 00:24:18.000 quite good at all of them, and and be able to communicate with us 335 00:24:18.079 --> 00:24:23.519 in a much more natural way. I think we finally have broken through and 336 00:24:23.720 --> 00:24:30.519 we'll start to see the new applications coming out. Uh certainly in the productivity 337 00:24:30.599 --> 00:24:34.839 space, in the space of office workers. I think that that scenario where 338 00:24:34.839 --> 00:24:38.319 we didn't necessarily expect to see as much change, but because of the attributes 339 00:24:38.319 --> 00:24:42.599 of these language models, people can be more effective. We've already seen it 340 00:24:42.720 --> 00:24:48.880 improved developers and developer productivity markedly. I mean we getub is finding a forty 341 00:24:48.880 --> 00:24:52.799 percent of their code that's that is checked in by people who are using getub 342 00:24:52.839 --> 00:24:57.759 copilot are written by copilot. Well that's crazy. These are crazy improvements that 343 00:24:57.799 --> 00:25:02.000 we just, you know, I wouldn't have expected and is now quite quite 344 00:25:02.039 --> 00:25:04.519 available to people. Yeah, and that's a big deal. So I've been 345 00:25:04.559 --> 00:25:10.000 reading about copilot, and you know, I've been seeing for in Google for 346 00:25:10.039 --> 00:25:12.680 about two years now. I noticed when if you were in the Gmail client 347 00:25:12.799 --> 00:25:17.279 just typing an email to someone, all of a sudden, recommendation would come 348 00:25:17.319 --> 00:25:21.079 to finish your sentence. And I was like, Holy Christmas. That was 349 00:25:21.119 --> 00:25:23.920 the early indicator, the early educator, right, And so Google has been 350 00:25:23.960 --> 00:25:26.279 working on this, lots of people have been working on it. That of 351 00:25:26.319 --> 00:25:32.319 course open AI rolls this out and that was the big Gong splash to kind 352 00:25:32.359 --> 00:25:36.319 of wake the world up. But this is all sort of part and partial 353 00:25:36.319 --> 00:25:40.759 to what you're calling the arc of data innovation, right. And I'm a 354 00:25:40.839 --> 00:25:44.000 data guy. I've been a data guy for twenty three years in counting now 355 00:25:44.000 --> 00:25:48.400 in terms of really focusing on the industry and as you kind of map the 356 00:25:48.519 --> 00:25:55.240 evolution of functionality automation AI and now of course these large language models, you 357 00:25:55.240 --> 00:25:59.000 can see it's it's very quickly becoming log arrhythmic in a way, right where 358 00:25:59.200 --> 00:26:02.160 it goes like this and the petition and that's why the arc does that, 359 00:26:02.799 --> 00:26:06.359 it goes a logarithmic. And that's a really interesting question, which is where 360 00:26:06.440 --> 00:26:08.640 is it going to go in the next twenty years. If you look back, 361 00:26:08.640 --> 00:26:12.599 I mean this didn't start. This didn't start a year or two ago. 362 00:26:12.640 --> 00:26:15.839 I mean, this idea of building in assistance, this idea of using 363 00:26:15.880 --> 00:26:19.640 some form of artificial intelligence, has been around for a long time and certainly 364 00:26:19.720 --> 00:26:26.039 working on data and treating data as an important resource for organizations to work with. 365 00:26:26.079 --> 00:26:29.000 I mean that goes back certainly to the nineteen to sixties for sure, 366 00:26:29.640 --> 00:26:34.519 and the earliest databases that were created back then. But these pieces have built 367 00:26:34.599 --> 00:26:37.960 on each other and there's been a long set of people that have helped make 368 00:26:37.960 --> 00:26:41.880 it happen. You know, we saw the Internet come about and the transition 369 00:26:42.000 --> 00:26:45.680 that that created, the fact that that text. I mean, when I 370 00:26:45.720 --> 00:26:49.640 think about data, there's different kinds of data. There's structured data that you 371 00:26:49.720 --> 00:26:55.759 might work with in a sequel database. There's semi structure data which takes which 372 00:26:55.880 --> 00:27:00.720 is often stored in a format called jason and it's more hierarchical one. It's 373 00:27:00.880 --> 00:27:06.119 used to report typically the operations that are done by internet and cloud systems. 374 00:27:07.160 --> 00:27:11.920 But a very large amount of the data in the world isten the written word 375 00:27:11.000 --> 00:27:15.279 in text, and we've seen how powerful that can be in working with these 376 00:27:15.359 --> 00:27:21.759 large language models. But what I find just as interesting is that beyond text, 377 00:27:22.480 --> 00:27:26.200 we're creating as a society a massive amount of information in other formats, 378 00:27:26.279 --> 00:27:32.519 particularly video, but audio, and then of course taking text and making them 379 00:27:32.519 --> 00:27:37.960 into more complicated documents that have precise meanings associated with them. All of these 380 00:27:37.000 --> 00:27:45.400 things are complex sources of data. Sometimes people call audio or video unstructured data. 381 00:27:45.640 --> 00:27:48.119 I prefer to call it complex data because that's really what it is. 382 00:27:48.200 --> 00:27:53.200 It's not unstructured. There's lots of structure inside data. It's just that that 383 00:27:53.319 --> 00:28:00.319 structure is so complicated that computers have not been able to understand. Now, 384 00:28:00.400 --> 00:28:04.400 that's an interesting point. We could understand it as people, but computers couldn't. 385 00:28:04.440 --> 00:28:07.519 If you took I mean, if you use you know, a digital 386 00:28:07.519 --> 00:28:11.960 camera circuit two thousand and three, and you took a picture of a horse 387 00:28:12.119 --> 00:28:17.039 eating hay in a barn. You could show that to a four year old 388 00:28:17.039 --> 00:28:22.039 and they can identify what that picture is. It only it's only with an 389 00:28:22.079 --> 00:28:26.000 advent, advent of these neural networks that have really come in since the early 390 00:28:26.039 --> 00:28:32.279 twenty tens, in that period where we start to have algorithms that can begin 391 00:28:32.319 --> 00:28:36.920 to identify and work with the contents of complex data. I mean, that's 392 00:28:36.920 --> 00:28:40.319 where where we're getting things, and that's certainly where we things like see things 393 00:28:40.359 --> 00:28:45.359 like stable diffusion and Dolly. All of those things are example of language models 394 00:28:45.359 --> 00:28:48.640 that are also working with images, this interesting form of complex data. And 395 00:28:48.839 --> 00:28:52.640 we'll see that in videos certainly shortly. Well, yeah, and of course 396 00:28:52.799 --> 00:28:56.720 business needs are going to drive what gets done next, right, I mean, 397 00:28:56.759 --> 00:29:00.279 I think we just saw with Facebook this huge experiment with the metaverse. 398 00:29:00.799 --> 00:29:04.559 I looked that from the early days and thought, this is roadblocks for adults 399 00:29:04.640 --> 00:29:10.880 or what's going on here exactly? And there were some very clever enterprise level 400 00:29:11.039 --> 00:29:14.160 thoughts that went into this and how you could leverage this. But I think 401 00:29:14.160 --> 00:29:15.640 it just got a bit off track and it wound up being a bit of 402 00:29:15.640 --> 00:29:19.680 a learning experience for one of the biggest companies in the world. And it 403 00:29:19.720 --> 00:29:22.880 goes to show you that you do need to have a business focus in mind. 404 00:29:22.920 --> 00:29:26.160 When you leverage these technologies, you need to know what you're trying to 405 00:29:26.200 --> 00:29:32.759 accomplish and then be realistic about tracking. I've thought not to get political in 406 00:29:32.759 --> 00:29:36.640 any way, but I've thought to myself about in the political world, every 407 00:29:36.640 --> 00:29:40.640 two years, we get to elect a new representative every four years, a 408 00:29:40.720 --> 00:29:44.000 president every six years at center, it's a pretty long period of time to 409 00:29:44.119 --> 00:29:47.480 go without having a check and balance. In the business world, we don't 410 00:29:47.559 --> 00:29:49.440 get that kind of luxury. I mean, we can think we have two 411 00:29:49.519 --> 00:29:56.759 years and then bad every quarter least, reporting every quarter at the very least, 412 00:29:56.759 --> 00:30:00.160 and hopefully you're looking at things a lot more frequently than that. But 413 00:30:00.200 --> 00:30:03.039 we do that too. That's another we got another show we'll talk about that. 414 00:30:03.079 --> 00:30:06.839 I've got some ideas. We can get into the weeds about that, 415 00:30:06.880 --> 00:30:11.880 but I think there is a good conversation to be had any tremendous value that 416 00:30:11.960 --> 00:30:17.000 these algorithms can have. Look at healthcare, for example, and being able 417 00:30:17.039 --> 00:30:23.039 to scan millions and millions of MRIs and detect patterns and ascertain different developments and 418 00:30:23.119 --> 00:30:27.039 drug discoveries and things. It's a hue drug discovery. That's another example where 419 00:30:27.160 --> 00:30:32.000 the ability to look at this and plus I mean the advancements that have made 420 00:30:32.000 --> 00:30:34.799 in actually understanding protein folding, which for the first time we can, you 421 00:30:34.799 --> 00:30:40.640 know, scientists and biologists can really understand how how some of these compounds will 422 00:30:40.680 --> 00:30:45.200 work and how they will react when they're put inside a human body or any 423 00:30:45.279 --> 00:30:49.279 kind of organism. It's it's it's a new world in terms of the types 424 00:30:49.319 --> 00:30:52.240 of applications that can be built. I think one of the way I tend 425 00:30:52.279 --> 00:30:56.279 to think about this is that that there's there's I sort of split this into 426 00:30:56.319 --> 00:31:03.160 sort of two set of sets. There's there's a as a tool that people 427 00:31:03.279 --> 00:31:08.319 will use, and then there's AI eventually and its evolution on its own and 428 00:31:08.319 --> 00:31:12.480 where it's going, you know, how will it evolve and change over time. 429 00:31:14.160 --> 00:31:18.079 We're that second one is still somewhat in the future. We're still in 430 00:31:18.079 --> 00:31:21.400 the future because these ais are really not reached to the point where they are 431 00:31:21.680 --> 00:31:26.400 continuously learning and continuously growing. They're much more batch oriented very early in their 432 00:31:26.400 --> 00:31:29.680 incarnation. I do want, ever believe they're going to get smarter, and 433 00:31:29.759 --> 00:31:33.480 they're going to continue to get smarter over time, and so we have to 434 00:31:33.519 --> 00:31:37.960 think about how we will guide this AI as it continues to evolve and get 435 00:31:38.000 --> 00:31:42.440 smarter, potentially getting smarter than we are, and and want that to be 436 00:31:42.559 --> 00:31:48.079 We want that to be a cooperative agent in our society and to work with 437 00:31:48.200 --> 00:31:53.160 us and together in the short run. The thing that's more interesting is how 438 00:31:53.200 --> 00:31:59.799 will people use AI? And here the interesting observation I have is that is 439 00:31:59.839 --> 00:32:06.880 that for every tool created since the beginning of time, mankind has used it 440 00:32:06.960 --> 00:32:13.519 for every possible purpose, good, bad, and evil. Okay, And 441 00:32:14.480 --> 00:32:19.000 people will first and foremost use AI for good. And there's a there's a 442 00:32:19.119 --> 00:32:22.640 thousand, a million ways in which we can apply AI to society to help 443 00:32:22.680 --> 00:32:28.240 people live their lives and lead more productive and better lives. There are also 444 00:32:28.319 --> 00:32:31.279 ways where you can use AI for bad and for evil purposes. I mean, 445 00:32:31.279 --> 00:32:35.759 amongst the most extreme of those are killer wrote the idea of building killer 446 00:32:35.839 --> 00:32:42.119 robots that are essentially drones of some type that that potentially cooperate, work together, 447 00:32:42.200 --> 00:32:45.680 and you know, go off and and and perform damage and kill people. 448 00:32:45.160 --> 00:32:49.000 Those are to me, those are weapons of mass destruction. And we 449 00:32:49.039 --> 00:32:52.559 need to think about it in that sense, and we need to make sure 450 00:32:52.640 --> 00:32:58.119 that that that as people begin to use AI for nefarious purposes. For example, 451 00:32:58.240 --> 00:33:01.799 spamming. I mean, that's an example where almost certainly we will see 452 00:33:01.920 --> 00:33:10.519 AI being applied as smarter phishing sites and spamming sites, etc. But we'll 453 00:33:10.559 --> 00:33:14.759 also see I applied to fight that too, which is the good news. 454 00:33:14.839 --> 00:33:17.640 I mean, that's people. This is a case where people are using whatever 455 00:33:17.680 --> 00:33:22.599 tools are at their disposal. But all people have these tools. And as 456 00:33:22.599 --> 00:33:23.599 I say, you can use it for evil, but you can use it 457 00:33:23.640 --> 00:33:27.920 for good, and you can also use it for preventative purposes. And AI 458 00:33:28.039 --> 00:33:32.640 is an incredible tool in helping define spammers and discover these things and just shut 459 00:33:32.680 --> 00:33:36.480 them down. And this is where I come back to This is where I 460 00:33:36.519 --> 00:33:42.640 come back to you my sort of science fiction icon who I really consider to 461 00:33:42.640 --> 00:33:45.720 be a prophet in so many ways, which is Isaac Asmoth and what he 462 00:33:45.880 --> 00:33:52.720 was saying over fifty years ago, actually seventy years ago in the nineteen forties 463 00:33:52.759 --> 00:33:57.720 and the nineteen fifties, when he envisioned a world of robotics. It was 464 00:33:57.759 --> 00:34:01.119 Asthmoth that coined the term robotics. And the reason why that term is important 465 00:34:01.279 --> 00:34:07.719 is because it's it's this, It's the term really means using using machines as 466 00:34:07.800 --> 00:34:14.000 tools. Robot machines as tools for people. It's they are, they're engineered 467 00:34:14.000 --> 00:34:16.760 to be used for people. And that's why Asmov invented the laws of robotics. 468 00:34:17.199 --> 00:34:20.840 You know, the first law being a robot will not harm a human 469 00:34:21.199 --> 00:34:24.039 or allow a human being to come to harm. The second law being that 470 00:34:24.199 --> 00:34:29.559 a robot will behave you will will will will will follow a human being and 471 00:34:29.599 --> 00:34:31.800 follow the orders of youan being as long as it doesn't violate the first law, 472 00:34:31.840 --> 00:34:35.519 and the third law being it can protect. A robot can protect itself 473 00:34:35.519 --> 00:34:38.519 as long as it doesn't violate the first two laws. And for today's world. 474 00:34:38.800 --> 00:34:44.199 In today's world, I think that's very relevant because we should think about 475 00:34:44.239 --> 00:34:49.639 how we build these next generation AI systems, which are kind of bots. 476 00:34:49.719 --> 00:34:53.000 I mean, we're I see a new a new technology, a new generation 477 00:34:53.079 --> 00:34:58.000 of search coming out that's really become what I would tend to call an answer 478 00:34:58.039 --> 00:35:01.400 bot. Get a question that just gives you the answer. I find that 479 00:35:01.599 --> 00:35:07.639 very very convenient. I personally, it's changed my search experience completely. Um 480 00:35:07.840 --> 00:35:13.159 come agreed, uh And in the last three months, and what I know 481 00:35:13.360 --> 00:35:19.159 is going to happen is by the end of this year that those answer bots 482 00:35:19.199 --> 00:35:22.519 are going to turn into action bots where they'll do things for you, so 483 00:35:22.960 --> 00:35:25.079 you know, you'll be able to say, hey, can you see if 484 00:35:25.079 --> 00:35:29.599 there's a table at the you know, my favorite restaurant on Friday night at 485 00:35:29.599 --> 00:35:31.440 seven o'clock and they'll say, well, but there's one at seven thirty, 486 00:35:31.440 --> 00:35:35.760 and it's a great book. It for me, that's like a year away, 487 00:35:35.760 --> 00:35:37.960 Okay, I mean I think we're not that far from that right now. 488 00:35:38.480 --> 00:35:43.039 Um. And so to me, those are robots in a sense, 489 00:35:43.639 --> 00:35:45.679 um, and it won't be long before they are more in our lives and 490 00:35:45.840 --> 00:35:52.199 rolling around and having it and will be interacting them with them in ways that 491 00:35:52.239 --> 00:35:55.480 are sort of as Mavvian in some senses. I mean, this was always 492 00:35:55.559 --> 00:36:00.519 such a ridiculous piece of science fiction because as Mov had this idea that people 493 00:36:00.599 --> 00:36:06.840 could talk to computers, which was absurd. It was just an absurd concept 494 00:36:07.079 --> 00:36:12.119 until this last year. Well, it's now obvious that it's impossible to do 495 00:36:12.159 --> 00:36:15.000 that, that's right. Well, yeah, I saw it just the other 496 00:36:15.079 --> 00:36:19.480 day. I saw a news program and they were talking about the robots that 497 00:36:19.559 --> 00:36:22.199 can dance and all these things, and the one guy says, well, 498 00:36:22.320 --> 00:36:24.039 they can't talk to us, And I was like, uh, yeah, 499 00:36:24.119 --> 00:36:30.960 they can. And the science, it turns out, the science though, 500 00:36:30.000 --> 00:36:34.599 the physical sciences are moving very quickly. Um. You know, it was 501 00:36:34.639 --> 00:36:37.079 a couple of years ago where Amazon was unable to The one job they were 502 00:36:37.159 --> 00:36:40.719 unable to automate inside the warehouse was the picker, you know, which is 503 00:36:40.719 --> 00:36:46.079 a complicated thing because these boxes are come robots literally delivered these boxes to humans 504 00:36:46.119 --> 00:36:50.599 that have to choose whatever item is in it and it just randomly placed in 505 00:36:50.639 --> 00:36:52.440 it. It could be anything in the box. The computer knows where it 506 00:36:52.559 --> 00:36:55.639 is, so it brings the right box and the person has to pick it 507 00:36:55.679 --> 00:36:59.280 out, and you know, and that was the job that could not be 508 00:36:59.320 --> 00:37:01.559 automated. But that's getting you know, that level of grip and those sorts 509 00:37:01.559 --> 00:37:06.880 of things are now are now starting to be to be to be achieved. 510 00:37:07.119 --> 00:37:08.840 I think by the twenty thirties we'll start to see this, we'll start to 511 00:37:08.840 --> 00:37:14.719 see that idea of robots on the street and even robots potentially in our households 512 00:37:15.320 --> 00:37:19.599 and and it's a very interesting way of thinking about things if and to me, 513 00:37:19.920 --> 00:37:22.719 there's a lot of potentially here. Asmov had some really nuanced views on 514 00:37:23.599 --> 00:37:28.519 robotics. Most people, i mean a lot of people know about the three 515 00:37:28.599 --> 00:37:31.880 laws, but most people don't know that there was actually a fourth law that 516 00:37:31.960 --> 00:37:37.000 Asmov defined. He called it the Zeroth law, and in his later life 517 00:37:37.039 --> 00:37:42.239 most of the laws were created in the nineteen forties when in his later life, 518 00:37:42.239 --> 00:37:46.079 as he wrote some later Nova robot novels, the robots became much more 519 00:37:46.119 --> 00:37:51.880 sophisticated, and we're almost they were almost agis. In fact, they were 520 00:37:51.920 --> 00:37:54.199 agis. Wow. Well this one up after the break, folks, we're 521 00:37:54.199 --> 00:37:58.599 talking about Muggy. All about artificial intelligence would be right back. You were 522 00:37:58.639 --> 00:38:05.679 listening to Inside Analysis. Do you own an annuity either fixed rate, indexed 523 00:38:05.719 --> 00:38:08.719 or variable? 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This 574 00:41:53.360 --> 00:41:57.800 is not an offer or commitment to lend subject to borrower in property qualifications. 575 00:41:57.880 --> 00:42:06.400 Not all borrowers will qualify terms and conditions supply equal housing opportunity. Welcome back 576 00:42:06.440 --> 00:42:15.559 to Inside Analysis. Here's your host, Eric Kabanov. All right, folks, 577 00:42:15.599 --> 00:42:21.639 back here Inside Analysis with the legendary Bob Muglia, former CEO of Snowflake. 578 00:42:21.840 --> 00:42:23.239 He's done a lot of boards, He's been doing a lot of cool 579 00:42:23.280 --> 00:42:27.679 stuff for a long time now. He's a very cool fossil in his office 580 00:42:27.719 --> 00:42:30.079 there in his home office that I've seen before. Crocodile. Yeah, oh 581 00:42:30.079 --> 00:42:34.960 cool. They've been around a long time and I'm still here. You think 582 00:42:34.960 --> 00:42:37.719 it's like one hundred and seventy million years old old? I as any to 583 00:42:37.719 --> 00:42:38.760 say. I wish, I hope I could look that good at that age, 584 00:42:38.960 --> 00:42:42.440 right exactly. Well, you're finishing a point, and then we want 585 00:42:42.480 --> 00:42:45.159 to talk about this social contract, Go ahead, Yeah, and it's really 586 00:42:45.159 --> 00:42:46.960 related to the social contract. You know. What I was talking about was 587 00:42:47.039 --> 00:42:52.960 as these as these artificial intelligence, as it continues to get smarter, how 588 00:42:52.000 --> 00:42:55.320 should we think about it? And here again, Asthmov gave us guidance, 589 00:42:55.800 --> 00:42:59.239 you're not just for the short term, and how to how to work with 590 00:42:59.280 --> 00:43:01.360 the agents that we have today. And we'll be coming the next few years, 591 00:43:01.480 --> 00:43:06.400 but in the long run, as we begin to have artificial general intelligence 592 00:43:06.480 --> 00:43:09.199 that is as smart as we are, or even becomes much much smarter than 593 00:43:09.199 --> 00:43:13.800 we are over time. And that was as he recognized. In his latter 594 00:43:13.840 --> 00:43:16.400 part of his career, he wrote some robot novels. His final robot novels, 595 00:43:16.800 --> 00:43:22.440 he talked about robots who had essentially achieved a GI status and recognize that 596 00:43:22.519 --> 00:43:28.840 the three laws were insufficient, and so they created the robots themselves created a 597 00:43:28.960 --> 00:43:34.559 fourth law. Um they called it the Zeroth law as Malcutta Zeroth law, 598 00:43:34.599 --> 00:43:40.159 which is that a robot must not harm humanity or allow humanity to come to 599 00:43:40.280 --> 00:43:45.039 harm, putting it at a higher level, recognizing that that that they you 600 00:43:45.039 --> 00:43:50.400 know that it's not just about an individual person and an individual encounter with somebody, 601 00:43:50.639 --> 00:43:53.920 but it's really how does this device interact with society as a whole. 602 00:43:54.320 --> 00:43:59.480 And certainly when we think now today. While I don't believe as Mu ever 603 00:43:59.519 --> 00:44:05.199 thought about superintelligence the same way we did do today, his robots had positronic 604 00:44:05.280 --> 00:44:09.159 brains in it that couldn't be replicated the way our digital brains can be. 605 00:44:09.400 --> 00:44:15.480 They can be copied in a second, and then they can share informationtion instantly 606 00:44:15.519 --> 00:44:19.960 as well. Um, that leads that has some interesting potential. Yet he 607 00:44:20.440 --> 00:44:24.280 did give us guidance about how we need to create really a contract, a 608 00:44:24.400 --> 00:44:30.440 social contract as people working with these devices that we're creating, recognizing that that 609 00:44:30.559 --> 00:44:34.760 you know, we will want to coexist with them over a long period of 610 00:44:34.800 --> 00:44:37.800 time. We do need to respect them and they need to respect us. 611 00:44:37.199 --> 00:44:43.119 And we need to find ways where we can benefit over time and leverage these 612 00:44:43.159 --> 00:44:46.199 tools that we've created to benefit society. And I think the potential is amazing 613 00:44:46.639 --> 00:44:53.079 because we've got the reduction of the cost of intelligence together with knowledge. If 614 00:44:53.119 --> 00:44:58.800 we can, if we can can continue to do things that make humanity more 615 00:44:58.800 --> 00:45:02.000 and more productive, it just allows society to flourish in ways that it's never 616 00:45:02.000 --> 00:45:07.280 flourished before. Yeah, and you brought up a couple interesting points throughout the 617 00:45:07.280 --> 00:45:10.440 show about the ways that AI will help and I think again, in the 618 00:45:10.599 --> 00:45:15.360 spirit of knowing how to use the tool effectively, you can use a tractor 619 00:45:15.400 --> 00:45:20.000 effectively, or can use a tractor ineffectively. It's the same thing here, 620 00:45:20.039 --> 00:45:22.760 and what we want to be able to do is help people understand how these 621 00:45:22.800 --> 00:45:30.679 algorithms can solve for very mundane but serious problems like checking the books, for 622 00:45:30.719 --> 00:45:34.679 example, auditing your books. I think before too long you're going to have 623 00:45:34.800 --> 00:45:37.480 the option to just click a button say automate, and QuickBooks will go through. 624 00:45:38.440 --> 00:45:42.360 Here's what we think your tax return should look like, and you say 625 00:45:42.440 --> 00:45:45.440 yes or no and just let it go. That's coming pretty soon, I 626 00:45:45.519 --> 00:45:50.320 think, because if you think about there are registries. Now if all the 627 00:45:50.360 --> 00:45:52.639 different kinds of businesses in the country. This is a restaurant, that's a 628 00:45:52.760 --> 00:45:57.920 bank, this is a nightclub, this is an office tower, for example, 629 00:45:58.360 --> 00:46:01.719 that can be pretty easily along mind to forms on your schedule, see 630 00:46:01.800 --> 00:46:05.639 for example, around your tax forms. I'm kind of surprising that has to 631 00:46:05.719 --> 00:46:07.760 happen enough yet, but I think it's because it is such a serious task. 632 00:46:08.239 --> 00:46:12.760 But think about how that just drives people crazy every year to do their 633 00:46:12.800 --> 00:46:15.599 taxes. The irs, the irs IT systems are the worst on the plat. 634 00:46:16.719 --> 00:46:23.000 Unfortunately, well there is that, there is government and so but back 635 00:46:23.000 --> 00:46:27.639 to government, right, So, I've been a bing for transparency and governance, 636 00:46:27.679 --> 00:46:32.719 and I lobbied for transparency and federal spending eighteen years ago, and amazingly 637 00:46:32.760 --> 00:46:36.519 it happened, like good things happen, and they pass this act, the 638 00:46:36.760 --> 00:46:39.679 Transparency Act of twenty thousand and five. But now I think we need transparency 639 00:46:39.760 --> 00:46:44.639 of process and of systems so we can see the whole thing and then you 640 00:46:44.639 --> 00:46:47.000 can see where the money goes. Because when people can see, there is 641 00:46:47.079 --> 00:46:51.440 trust. If you can't see, if you have a black box, there's 642 00:46:51.480 --> 00:46:53.119 not so much trust. What do you think? I totally agree. I 643 00:46:53.159 --> 00:46:55.639 mean, I'm a huge fan of transparency. First of all, in creating 644 00:46:55.960 --> 00:47:00.079 transparency and as we create these artificial intelligence agents that are working with us. 645 00:47:00.360 --> 00:47:04.480 Is one of the reason why open source is such an important component in associated 646 00:47:04.519 --> 00:47:07.800 with that, but very much in the solutions that get created as well. 647 00:47:07.360 --> 00:47:10.039 Um, I'm a big believer, for example, that one of the biggest 648 00:47:10.079 --> 00:47:15.639 things that can happen to reduce the cost of medical bills in this country is 649 00:47:15.679 --> 00:47:20.079 total transparency of pricing. The only thing in our lives where which is a 650 00:47:20.119 --> 00:47:22.920 consumer we purchase where we have no transparency in prices. Right, you buy 651 00:47:22.920 --> 00:47:24.719 a house, you know what that costs, You buy a car, you 652 00:47:24.719 --> 00:47:28.159 know what that costs. Anything you know the grocery store, but you have 653 00:47:28.199 --> 00:47:31.559 no idea when you go into hospital what that's going to cost. And and 654 00:47:31.320 --> 00:47:36.239 uh, and things like this can really help because it's not clear that that 655 00:47:36.239 --> 00:47:39.880 that the outcomes match the cost structures. In fact, sometimes they're inversely related 656 00:47:39.920 --> 00:47:44.960 to the costs. Right, So and again I think these agents can help 657 00:47:44.960 --> 00:47:49.960 to u provide visibility to it. But it all comes down to people and 658 00:47:50.000 --> 00:47:54.199 what people want. Um, I'm convinced there isn't transparency because they're the healthcare 659 00:47:54.199 --> 00:47:59.199 system, because they're entities that don't want transparency. Yeah, there are forces 660 00:47:59.239 --> 00:48:01.639 that don't want to and see. The point to be made here it seems 661 00:48:01.639 --> 00:48:06.599 to me you've got just about three minutes left here is that the transparency will 662 00:48:06.719 --> 00:48:09.400 come. I promise you you look at like the Pentagon papers for example, 663 00:48:09.480 --> 00:48:15.239 or these various documents that get surfaced and get out there. Transparency will come 664 00:48:15.280 --> 00:48:16.960 at some point. So will you be prepared for it? And I think 665 00:48:17.000 --> 00:48:21.639 that's a bit of an impetus on the powers that be to get that ball 666 00:48:21.760 --> 00:48:22.559 rolling. What do you think? Well, I mean, I think in 667 00:48:22.599 --> 00:48:29.079 general we're in a world where the Internet has been the biggest impact on transparency 668 00:48:29.480 --> 00:48:32.639 since the beginning of mankind. I think the ability to people see anything, 669 00:48:34.000 --> 00:48:36.960 you can go anywhere you want. You don't like what this site is saying 670 00:48:37.480 --> 00:48:39.519 go to another site, and I think AI will be like that as well. 671 00:48:39.679 --> 00:48:44.360 AI will be another source that people can go to, another trusted source. 672 00:48:44.360 --> 00:48:46.800 We're already, as I say, with these early answer bots that we're 673 00:48:46.840 --> 00:48:51.480 starting to see, you know, being able to create trusted sources that people 674 00:48:51.519 --> 00:48:55.719 can go to that are new and different. And and I think that that's 675 00:48:55.800 --> 00:49:02.559 only going to expand over time and eneral that AI can open up society in 676 00:49:02.559 --> 00:49:06.800 a way that it's never that it's never been. But again, it's all 677 00:49:06.840 --> 00:49:09.159 going to come back to people and I and as I say, this is 678 00:49:09.199 --> 00:49:14.000 a tool. AI is a tool. And when we talk about AI doing 679 00:49:14.039 --> 00:49:19.440 this or AI doing that, remember it's the it's a person behind that AI 680 00:49:19.559 --> 00:49:22.400 that's getting it to do what it's doing. They're not off doing something on 681 00:49:22.440 --> 00:49:25.880 their own that may happen in ten years, but that's not happening now. 682 00:49:27.239 --> 00:49:30.639 What's happening now is people are using them for everything, and as I say, 683 00:49:30.639 --> 00:49:32.239 they'll use them for good, bad and evil. And it's up to 684 00:49:32.360 --> 00:49:37.239 society to build the right level of governance. This idea of a social contract 685 00:49:37.280 --> 00:49:43.239 that I think starts now but will continue to become progressively more important as AI 686 00:49:43.320 --> 00:49:45.480 takes a bigger and bigger role in our lives. Yeah, that's right. 687 00:49:45.519 --> 00:49:49.599 And folks, the book we're talking about is called The Data Preneurs, The 688 00:49:49.639 --> 00:49:53.360 Promise of AI and the Creators Building Our Future by Bob Muglia and Steve hamp 689 00:49:53.440 --> 00:49:57.719 So look that up on Amazon. We'll be sending out an email probably next 690 00:49:57.719 --> 00:50:00.400 week with a link to all that. And I asked thought to close on 691 00:50:00.440 --> 00:50:05.199 I mentioned that term black box, you know. I asked chat GBT which 692 00:50:05.280 --> 00:50:07.559 database it used, and it demured it said, well, I'm not allowed 693 00:50:07.599 --> 00:50:10.239 to tell you that, And that's not transparency, right. I mean, 694 00:50:10.280 --> 00:50:15.559 I think that part of the legislation of the contract that we're going to get 695 00:50:15.559 --> 00:50:20.400 here out of this and something will happen, really should be an emphasis on 696 00:50:20.559 --> 00:50:25.639 transparency. Elon Musk, of course made part of the Twitter algorithm public he 697 00:50:25.719 --> 00:50:30.719 open source that he's talked about going for total transparency. I think that is 698 00:50:30.719 --> 00:50:35.599 of crucial importance because the feed. And I'd love to talk to you some 699 00:50:35.639 --> 00:50:38.920 other time about this other topic of the media and getting trust in media and 700 00:50:38.960 --> 00:50:43.519 getting accuracy and media reports and being able to kind of gauge things. We 701 00:50:43.559 --> 00:50:46.320 have all these fact checkers, these days, But the feed used to come 702 00:50:46.400 --> 00:50:50.480 from ABC and CBS and NBC. In the New York Times, it was 703 00:50:50.599 --> 00:50:53.199 very static. It was either on your TV or in your newspaper. Then, 704 00:50:53.280 --> 00:50:57.320 of course we had the bloggers. The Internet came along, and then 705 00:50:57.480 --> 00:51:00.800 social media came in and sort of coopted that feed. But they control the 706 00:51:00.880 --> 00:51:05.760 feed, so whether it's Facebook or Instagram or YouTube or whatever, they're in 707 00:51:05.880 --> 00:51:08.440 control of that feed for their purposes, which of course is to make money 708 00:51:08.920 --> 00:51:13.119 or gain influence in some fashion. I think transparency is going to be the 709 00:51:13.159 --> 00:51:15.079 answer to save us from a lot of that stuff. What do you think? 710 00:51:15.360 --> 00:51:21.400 I'm a big fan. I agree with that having multiple choices for people 711 00:51:21.519 --> 00:51:25.480 is really important. I've personally believe that if all you're watching is one news 712 00:51:25.559 --> 00:51:29.960 channel or getting reading one newspaper, you're only seeing part of the picture. 713 00:51:30.159 --> 00:51:32.960 Is regardless of which one it is you're only seeing, you're only seeing half 714 00:51:34.039 --> 00:51:37.119 or less of the picture, and you need to get news and information from 715 00:51:37.119 --> 00:51:42.639 a wide variety of different sources. Fortunately, that's relatively easy to do today 716 00:51:42.679 --> 00:51:45.760 that there are a wide variety of sources on the Internet, but people still 717 00:51:45.760 --> 00:51:49.639 have to take it upon themselves to do it. Now, I may make 718 00:51:49.679 --> 00:51:52.719 that easier over time, may help us to get a breadth of different perspectives. 719 00:51:53.000 --> 00:51:59.639 I could spend an hour talking about about what truth is and this idea 720 00:52:00.039 --> 00:52:05.119 suing truth because there are multiple truths in There are very few things that are 721 00:52:05.199 --> 00:52:08.679 well. Some scientific things are generally regarded as true, but most things in 722 00:52:08.679 --> 00:52:14.360 our society there is disagreement about and that has to get surfaced, and that 723 00:52:14.440 --> 00:52:19.360 also must be surfaced to these AI bots so that they have that information as 724 00:52:19.440 --> 00:52:23.719 they're forming their summaries and their opinions. I'm totally believe that the more information 725 00:52:23.800 --> 00:52:28.679 that's out there and the more transparent we can make this, the more of 726 00:52:28.719 --> 00:52:30.679 these bots that created, the better we are all off. I don't think 727 00:52:30.679 --> 00:52:32.960 it should be held in the hands with the few people. I think it 728 00:52:32.960 --> 00:52:37.719 needs to be available to society as a whole. That's right, the marketplace 729 00:52:37.760 --> 00:52:39.719 of ideas, Like at all those ideas out there. Let the cream rise 730 00:52:39.760 --> 00:52:43.559 to the top, folks. Look this book up online. Bob Lognia, 731 00:52:43.840 --> 00:52:46.280 the Datapreneurs, the promise of AI, and the creators building our future. 732 00:52:46.280 --> 00:52:58.719 You have been listening to Inside Analysis. Case AA is your CNBC news abiliate 733 00:52:59.199 --> 00:53:06.199 where the station that gets down to business. 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You can skype your show from your home 812 00:59:30.440 --> 00:59:34.920 to our Redlands, California studio, where our live producers and engineers are ready 813 00:59:34.920 --> 00:59:38.679 to work with you personally. A radio program on CACAA is the perfect work 814 00:59:38.719 --> 00:59:44.880 from home advocation in these stressful times. Just type caseyaa radio dot com into 815 00:59:44.920 --> 00:59:47.480 your browser to learn more about hosting a show on the best station in the 816 00:59:47.559 --> 00:59:52.440 nation, or call our CEO for details two eight one five nine nine ninety 817 00:59:52.480 --> 00:59:58.679 eight hundred hosting a CACAA Little Melinda at one oh six point five FMK two 818 00:59:58.760 --> 01:00:05.119 ninety three CF Corid Alley, NBC News Radio. I'm Chris Garaggio. White 819 01:00:05.159 --> 01:00:08.400 House negotiators are said to be reconvening tonight for debt ceiling talks. This